Create app.py

app.py

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+import torch
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+
+repo_name = "Jayanth2002/dinov2-base-finetuned-SkinDisease"
+image_processor = AutoImageProcessor.from_pretrained(repo_name)
+model = AutoModelForImageClassification.from_pretrained(repo_name)
+
+# Load and preprocess the test image
+image_path = "/content/img_416.jpg"
+image = Image.open(image_path)
+encoding = image_processor(image.convert("RGB"), return_tensors="pt")
+
+# Make a prediction
+with torch.no_grad():
+    outputs = model(**encoding)
+    logits = outputs.logits
+
+predicted_class_idx = logits.argmax(-1).item()
+
+# Get the class name
+class_names = ['Basal Cell Carcinoma', 'Darier_s Disease', 'Epidermolysis Bullosa Pruriginosa', 'Hailey-Hailey Disease', 'Herpes Simplex', 'Impetigo', 'Larva Migrans', 'Leprosy Borderline', 'Leprosy Lepromatous', 'Leprosy Tuberculoid', 'Lichen Planus', 'Lupus Erythematosus Chronicus Discoides', 'Melanoma', 'Molluscum Contagiosum', 'Mycosis Fungoides', 'Neurofibromatosis', 'Papilomatosis Confluentes And Reticulate', 'Pediculosis Capitis', 'Pityriasis Rosea', 'Porokeratosis Actinic', 'Psoriasis', 'Tinea Corporis', 'Tinea Nigra', 'Tungiasis', 'actinic keratosis', 'dermatofibroma', 'nevus', 'pigmented benign keratosis', 'seborrheic keratosis', 'squamous cell carcinoma', 'vascular lesion']
+predicted_class_name = class_names[predicted_class_idx]
+
+print(predicted_class_name)